There is a significant number of visually impaired individuals who suffer sensitivity
loss to high-spatial frequencies, for whom current optical devices are
limited in degree of visual aid and practical application, Digital image and
video processing offers a variety of effective visual enhancement methods that
can be utilised to obtain a practical embedded augmented vision device such
as a head mounted display device, Common approaches to augmented vision
extract an images high-spatial frequencies through digital image processing
edge detection techniques, which are then overlaid on top of the original image
to improve visual perception amongst the visually impaired, Augmented
visual aid devices require highly user-customisable, real-time capable algorithms
designed for subjective configuration per task, where current digital
image processing visual aids offer very little user-configurable options,
Firstly, the effectiveness of various digital image edge detection techniques
through augmented image visual experiments with simulated low-vision subjects
are investigated, A comparitive study of the 6416 DSP and Virtex-5
FPGA embedded platforms are evaluated for performance benchmarks of
implemented edge detectors of various complexity. In addition to optimising
the mathematically complex statistical edge detection algorithm for embedded
implementation. A highly user-customisable real-time morphology
edge enhanced augmented vision algorithm and FPGA realisation are presented,
where the edge type, magnitude and edge thickness can be modified during real-time operation. A reconfigurable morphological architecture for
real-time implementation on FPGA is developed, which obtains performance
comparable to other approaches, in addition to obtaining a significant degree
of reconfigurability not previously demonstrated in literature. A morphological
abstraction framework is presented, where images are significantly
abstracted to obtain efficient visual entropy through enhancing key edge
component information, while simultaneously reducing other image information.
The morphological abstraction framework is highly suited for FPGA
implementation, produces visually comparable results to equivalent methods,
while obtaining efficient computational complexity. A morphological
edge preserving smoothing filter is presented, which utilises adaptive structuring
element functions obtained from a counter-harmonic mean bilateral
filter that asymptotically corresponds to morphological operations.